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Background DNA methylation is an epigenetic modification known to play a prime role in gene silencing and is an important topic in epigenetic research. However, due to technology-dependent errors there are inconsistencies between methylation measurements from different methods [1]. Incorrect methylation calls could result in the discovery of spurious associations between methylation patterns and specific phenotypes in epigenomewide association studies (EWAS). We worked towards assigning a measure of confidence to individual CpGs to down-weigh or exclude positions with inconsistent measurements in such studies. We used methylation measurements from the Infinium HumanMethylation450 microarray (b450K) and whole genome bisulfite sequencing (bWGBS) to evaluate whether locus-specific measurement differences, Δb = b450K − bWGBS, are predictable using statistical learning techniques.

bmc bioinformatics

Identification and analysis of methylation call differences between bisulfite microarray and bisulfite sequencing data with statistical learning techniques